Systems and methods for hierarchical dual-dynamic exception management

ABSTRACT

Systems and methods for hierarchical dual-dynamic exception management are disclosed. In one embodiment, the system and method may provide: (1) hierarchical exception modelling, metric algorithms and aggregation; (2) node level exception metric algorithm and high-level status algorithm calculation; (3) node level investigation via “explain” of exception factors (e.g., focusing an analyst on a particular issue); (4) node level tracking workflow management through ability to add commentary, multiple-person signoff, and high-level status calculation overrides; (5) visibility, auditable compliance reporting; (6) dashboard for internal operational analysts/managers with full investigation view; (7) dashboard for external clients with sub views; (8) generic data interface to existing workflow management system; and (9) machine learning component such that the exception metric can be intelligently re-adjusted (for example, if there are too many false positives then make the thresholds more forgiving). Other features and/or advantages may be provided as is necessary and/or desired.

RELATED APPLICATIONS

The present application is claims priority to U.S. Provisional PatentApplication Ser. No. 62/159,558, the disclosure of which is herebyincorporated, by reference, in its entirety.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention generally relates to payment processing, and, morespecifically, to systems and methods for hierarchical dual-dynamicexception management.

2. Description of the Related Art

Accurate and timely unit pricing is an essential goal for fundaccountants. This task, however, is often hampered by vast and disparatedata, fixed exception thresholds, multiple daily workflows, andsignificant manual intervention.

A key aspect of any fund accountant process is the verification ofpricing components which make up the Net-Asset Value, or “NAV.” Ingeneral, fixed and constant thresholds are applied to the pricingcomponents. Thus, if the absolute change on a day of a value breachesthis tolerance, then an alert is triggered.

SUMMARY OF THE INVENTION

Systems and methods for hierarchical dual-dynamic exception managementare disclosed. In one embodiment, a method for hierarchical dual-dynamicexception management may include (1) an exception management systemcomprising at least one computer processor configuring a hierarchicalexception profile comprising a plurality of exceptions, wherein at leastone of the exceptions comprises a plurality of sub-exceptions, eachexception and sub-exception having at least an exception metric, ahigh-level status calculation, and an exception explain calculation; (2)the exception management system receiving, from a plurality of externaldata sources, net-asset value data, the net-asset value data comprisinga plurality of components and sub-components, each component associatedwith an exception, and each sub-component associated with asub-exception; (3) the exception management system calculating a currentdaily value of interest for the net-asset value data; (4) the exceptionmanagement system identifying at least one exception from thehierarchical exception profile based on the current daily value ofinterest; (5 the exception management system calculating a high-levelstatus and an explain for the identified exception from the hierarchicalexception profile; (6) the exception management system publishing theidentified exception, the calculated high level status, and thecalculated explain to at least one of an internal system and an externalsystem; and (7) the exception management system checking an entitlementof a user accessing the exception, the calculated high level status, andthe calculated explain before displaying results to the user.

In one embodiment, the plurality of external data sources may includeintra-day data sources and end-of-day data sources.

In one embodiment, the plurality of net-asset value data may include oneor more of fund data, portfolio data, income data, expense data, foreignexchange data, and/or benchmark data.

In one embodiment, the receipt of net-asset value data may trigger analert that new net-access value data is received.

In one embodiment, the high-level status may include a red level, anamber level, and/or a green level.

In one embodiment, the step of calculating a high-level status for anexception based on the daily value of interest may include setting abaseline based on historical observations of the daily value ofinterest; and setting the high-level status for the exception to anelevated status if the current daily value of interest exceeds thebaseline.

In one embodiment, wherein there is first threshold for negative values,and a second threshold for positive values.

In one embodiment, the explain may include a root cause for theexception.

In one embodiment, the method may further include the exceptionmanagement system receiving an override of the exception.

In one embodiment, the method may further include the exceptionmanagement system providing an internal operational analyst anexception, a high-level status for the exception, and the explain; theexception management system receiving feedback from the internaloperational analyst for the exception; the exception management systemproviding a machine learning component with the feedback; and themachine learning component of the exception management systemautomatically adjusting the threshold for the high-level status.

In one embodiment, the threshold may be increased in response to anumber of false positives above a predetermined number.

In one embodiment, the method may further include the exceptionmanagement system receiving a signoff from the internal operationalanalyst for a funds alert.

In one embodiment, the method may further include the exceptionmanagement system automatically adjusting a frequency of thresholdadjustment.

In one embodiment, the method may further include the exceptionmanagement system automatically persisting the metric value, RAG valueand RAG status, and root cause for the exception.

In one embodiment, hierarchical exception profile may be configurablebased on income, bond income, equity income, and/or margin calls andderivatives.

Embodiments of the present invention allow business operational analyststo define an Operational Exception Management (OEM) process in the formof hierarchical collection of trackable sub-exceptions. Each node of thehierarchy may be independently configurable with specific algorithms formetric and high-level status calculation. Each trackable sub-exceptionnode may provide investigative details to explain the factors causingthe exception. The exception workflow of each sub-exception may betracked and managed via commentary and signoff by operational analystsand/or operational managers, thereby providing visibility and auditablecompliance reporting. This may allow business operational managers toefficiently and effectively manage exception processes to minimize timeand moreover potential compensatory costs and disruption to deliver onagreed client Service Level Agreements (SLAs).

In one embodiment, an end-to-end streamlined exception management systemand process is disclosed. The system and method may provide: (1)hierarchical exception modelling, metric algorithms and aggregation; (2)node level exception metric algorithm and high-level status algorithmcalculation; (3) node level investigation via “explain” of exceptionfactors (e.g., focusing an analyst on a particular issue); (4) nodelevel tracking workflow management through ability to add commentary,multiple-person signoff, and high-level status calculation overrides;(5) visibility, auditable compliance reporting; (6) dashboard forinternal operational analysts/managers with full investigation view; (7)dashboard for external clients with sub views; (8) generic datainterface to existing workflow management system; and (9) machinelearning component such that the exception metric can be intelligentlyre-adjusted (for example, if there are too many false positives thenmake the thresholds more forgiving). Other features and/or advantagesmay be provided as is necessary and/or desired.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the present invention, the objectsand advantages thereof, reference is now made to the followingdescriptions taken in connection with the accompanying drawings inwhich:

FIG. 1a illustrates an example fund Change on Day (“CoD”) against abenchmark index CoD;

FIG. 1b illustrates a difference between fund price and index price;

FIG. 2 depicts a system for hierarchical dual-dynamic exceptionmanagement according to one embodiment;

FIG. 3 depicts a method for hierarchical dual-dynamic exceptionmanagement according to one embodiment;

FIG. 4 depicts an example of an exception profile according to oneembodiment; and

FIG. 5 depicts an example of an exception profile according to oneembodiment.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Several embodiments of the present invention and their advantages may beunderstood by referring to FIGS. 1-5.

In one embodiment, adaptive algorithms may be used to reduce operatingexpenses and risk, while increasing customer satisfaction. Many fundsexperience differences in positive and negative standard deviations,which empirically challenge the practice of using fixed and constantexception thresholds.

The verification of a fund's NAV price typically considers severalcomponents, such as income, expenses, shares class divergence, etc.,where each component may be considered as a time series of observations.Typically, this may be performed by calculating the change on day, orCoD, and comparing it to a pre-determined fixed and constant threshold.CoD may be calculated with the following equation:

$\frac{( {x_{t} - x_{t - 1}} )}{x_{t - 1}}{where}\mspace{14mu} x_{t}\mspace{14mu}{is}\mspace{14mu}{an}\mspace{14mu}{observed}\mspace{14mu}{value}\mspace{14mu}{on}\mspace{14mu}{day}\mspace{14mu} t$

Embodiments disclosed herein focus on fund-level benchmarking, comparingthe fund price change on day and benchmark price change on day. This maybe defined as:

$\frac{( {U_{t} + R_{t}} ) - ( {U_{t - 1} + R_{t - 1}} )}{N} - ( {B_{t} - B_{t - 1} - 1} )$

where U_(t) is the unrealized gain or loss on day t, R_(t) is therealized gain or loss on day t, N is the total net asset value aftercapital change, and B_(t) is the benchmark index price on day t.

FIG. 1a illustrates an example fund CoD against a benchmark index CoD,and FIG. 1b illustrates the difference between fund price and indexprice. Using a single constant threshold gives rise to false positives.As illustrated, there are more negative true positives than positivetrue positives.

In one embodiment, an empirical distribution (i.e., a distribution thatdoes not assume Gaussian behavior) may be calculated based on givendiscrete observations at certain dates. Using this distribution, theempirical standard deviation, or p-values, may be calculated. This maybe accomplished by first calculating a continuous empirical distribution(e.g., using kernel density estimation techniques, etc.). Next, thepositive and negative standard deviations, or p-values, may becalculated.

In one embodiment, rather than use a constant threshold, one or moreadaptive algorithms may be used. In one embodiment, the thresholds maybe discretely determined for positive and conditions, and may bere-calibrated periodically, as necessary, etc.

Referring to FIG. 2, a system for hierarchical exception management isdisclosed according to one embodiment. System 200 may include internaldata source(s) 210, external data source(s) 215, and hierarchicalexception management system 220. Hierarchical exception managementsystem 220 may include analyst/user 225, user interface 230, exceptiondata source 235, hierarchical exception calculator 240, feed gateway245, Extract, Transform, and Load (“ETL”) data 255, ODS data source 250,and trigger message 260.

In one embodiment, internal data source(s) 210 may include data sourceswith the financial institution, such as internal analysis, data, etc.External data source(s) 215 may include third party data sources, forexample, Bloomberg. Feed gateway 245 may receive data feeds frominternal data source(s) 210 and external data sources (215). In oneembodiment, feed gateway 245 may abstract different data transferprotocol from the different sources.

In one embodiment, ETL 255 may extract, transform, and load the datafrom feed gateway 250. ODS data source 250 may receive and storetransactional data.

Hierarchical exception calculator may perform hierarchical exception andcalibration calculations. In one embodiment, exception and calibrationresults may be stored in exception data store 235.

User interface 230 may display hierarchical exception and calibrationdata to, for example, analyst 225. Analyst 225 may review, manage,approve, etc. exceptions.

In one embodiment, user interface may be provided on a workstation, adesktop computer, a notebook computer, a tablet computer, a smart phone,or any other suitable electronic device.

Referring to FIG. 3, a method for hierarchical exception management isdisclosed according to one embodiment. In step 302, data may be receivedfrom one or more intraday source system. In one embodiment, the intradaysource systems are the systems of record for intraday data. In oneembodiment, the data may include data for one or more sub components ofNAV, such as fund, portfolio, income, expenses, foreign exchange (“fx”),benchmark data, etc. In one embodiment, the intraday source systems mayprovide the data in real time, or substantially in real time.

In step 304, data may be received from one or more non-real-time datasources, such as end-of-day source systems. The end-of-day systems mayprovide some, or all, of the data provided by the intraday sourcesystems, but may provide the data in batches, at the end of the day,overnight, etc.

In step 306, trigger data may be received. In one embodiment, thetrigger data may provide a message to the system that new data is loadedand available for processing. For example, data that is received fromintraday source systems and/or end-of-day source systems may initiate,or trigger, processing of the received data.

In step 308, an exception metric may be calculated. In one embodiment,the exception metric may be calculated so that a determination may bemade as to whether or not the metric exceeds some threshold ortolerance. In one embodiment, two values may be considered: the value ofthe interest in the metric, and the threshold or tolerance.

In one embodiment, the value of interest may be based on a configuration(e.g., income alert), may be the sum of all sources of income (equitydividends, bond coupons, etc.), etc.

Steps 308 a-308 f illustrate an example of the interest in the metriccalculation.

In step 308 a, a hierarchical exception may be configured. For example,a NAV may be broken down into sub components (e.g., market moves,income, expense and other movements such as FX, rates, etc.). Income maythen be broken down to bond or equity, then by region, and then bysector, etc. and hence forms a natural hierarchical model. The“configuration” allows these relationships to be modeled.

In step 308 b, a metric calculation algorithm may be applied. In oneembodiment, this algorithm may be used to calculate the daily value ofinterest. For example, income could be simply sum of all sources ofincome for a particular day or accrued over a certain time period.

In step 308 c, a high-level status calculation algorithm may be appliedto calculate the high-level status based on step 308 b. In oneembodiment, the high-level status may be a three-tier status, such asred-amber-green (RAG), a two-level status, such as go/no-go, good/bad,high/low, etc. a numerical status (e.g., 1-10), a grade status (e.g.,A-F), etc. Any suitable high-level status may be used as necessaryand/or desired.

In one embodiment, the high-level status calculation may be athreshold-based approach, for example where a baseline is calculatedusing historical observations of data in step 308 b. If the metriccalculated in step 308 b breaches the threshold, the high-level statusmay be “red.” If there is no breach, then the high-level status may begreen. An “amber” color may signify a warning, such as when data ismissing or there are non-critical breaches.

In one embodiment, there may be independent thresholds for both positiveand negative values we have independent thresholds.

In step 308 d, an exception explain calculation algorithm may beapplied. In one embodiment, this algorithm may calculate or determine aroot cause, or causes, of an alert. Using income as an example, thismight be a table where the rows represent each income line across allholdings. The rows would be sorted by income amount in descending order.

For example, if a total received coupon amount for a fund exceeds atotal expected coupon amount, a red alert may be raised. The analyst maythen review the “explain” table that may comprise rows of income linesthat may include several columns, such as previous income, currentincome, and difference. The columns may be sorted, for example, byincome difference, to focus the analyst on the largest difference.

Other methods and techniques for presenting exceptions to an analysismay be used as is necessary and/or desired.

In step 308 e, a high-level status calculation override may be applied.For example, a user and/or analyst may override the automatedcalculation of alerts if he or she believes that the algorithm hasincorrectly calculated the high-level status.

In step 308 f, dynamic calibration may configured. In one embodiment,the frequency of the calibration of the high-level status calculationalgorithm may be set.

After the exception metric is calculated in step 308, in step 310, ahigh-level status may be calculated. As noted above, the high-levelstatus may be a three-tier status, such as red-amber-green (RAG), atwo-level status, such as go/no-go, good/bad, high/low, etc. a numericalstatus (e.g., 1-10), a grade status (e.g., A-F), etc. Any suitablehigh-level status may be used as necessary and/or desired. The statusmay be calculated based on the algorithm described in step 308 c, above.

In step 312, the exception explain, described above with regard to step308 d, may be calculated.

In step 314, the metric value, high-level status, high-level statusvalue, etc., exception explanation, etc. may be stored. The complete,pre-calculated explain may be displayed when required or requested bythe user interface.

In one embodiment, in step 332, a check to make sure that thedestination system is authorized to receive the published exception maybe performed. If it is, in step 334, the exception may be published tothe destination systems. In one embodiment, external systems that maysubscribe to the results may receive the results. In one embodiment, anexternal system may be a wholesale customer that subscribes to the rawdata, the exception notification, etc.

In step 316, a check may be made to ensure that the data recipient isauthorized to receive the results may be made.

If, in step 316, an external client is authorized, in step 330, theresults may be proved to an external client This may be an independentview for external clients.

In addition, in step 318, the results may be provided to an internalanalyst via, for example, an internal analysis dashboard.

In one embodiment, the internal analyst dashboard may display alerts,status and explains. This may be independent from external user view,discussed below, as external clients may not be provided with alerts orexplains, but may only be provided with the alert value.

In step 320, workflow for managing the exception may be provided to theinternal analyst. In one embodiment, in step 322, the analyst may entercommentary, and in step 324, the analyst may signoff an exception.

In step 326, the analyst may provide alert validation. For example, theanalyst may provide feedback (e.g., true/false positives/negatives) tothe machine learning component.

In step 328, based on the feedback from the analyst, a machine learningcomponent may recalibrate the high-level status calculation algorithm.For example, if there are too many false positives, the outlierthresholds may be increased to be more forgiving.

In another embodiment, the machine learning component may refine thealgorithm(s) (e.g., the metric calculation algorithm, the explaincalculation algorithm, etc.) or may develop new algorithm(s) as isnecessary and/or desired.

Other machine learning may be used as is necessary and/or desired.

Referring to FIG. 4, an example of a hierarchical configuration of anexception profile according to one embodiment is provided. For example,exception profile 410 may include a plurality of exceptions (e.g.,exception 1, exception 2, and exception 3, etc.), and each of these canbe again broken down into sub-exceptions (e.g., exceptions 1.a, 1.b,3.a, etc.). Each sub-exception may include one or more of an exceptionmetric, a high-level status (e.g., RAG) calculation, and an explaincalculation.

In one embodiment, each sub-exception may be further broken intosub-exceptions, and so on (e.g., exceptions 1.b.i, 1.b.ii, etc.).

FIG. 5 depicts an example exception profile 510. In this embodiment,exception 1 may be income, exception 2 may be expense, and exception 3may be market move. Exception 1 may include sub-exceptions equity incomeand bond income, and sub-exception bond income may further includesub-exceptions treasury income and corporate bond.

Hereinafter, general aspects of implementation of the systems andmethods of the invention will be described.

The system of the invention or portions of the system of the inventionmay be in the form of a “processing machine,” such as a general purposecomputer, for example. As used herein, the term “processing machine” isto be understood to include at least one processor that uses at leastone memory. The at least one memory stores a set of instructions. Theinstructions may be either permanently or temporarily stored in thememory or memories of the processing machine. The processor executes theinstructions that are stored in the memory or memories in order toprocess data. The set of instructions may include various instructionsthat perform a particular task or tasks, such as those tasks describedabove. Such a set of instructions for performing a particular task maybe characterized as a program, software program, or simply software.

In one embodiment, the processing machine may be a specializedprocessor.

As noted above, the processing machine executes the instructions thatare stored in the memory or memories to process data. This processing ofdata may be in response to commands by a user or users of the processingmachine, in response to previous processing, in response to a request byanother processing machine and/or any other input, for example.

As noted above, the processing machine used to implement the inventionmay be a general purpose computer. However, the processing machinedescribed above may also utilize any of a wide variety of othertechnologies including a special purpose computer, a computer systemincluding, for example, a microcomputer, mini-computer or mainframe, aprogrammed microprocessor, a micro-controller, a peripheral integratedcircuit element, a CSIC (Customer Specific Integrated Circuit) or ASIC(Application Specific Integrated Circuit) or other integrated circuit, alogic circuit, a digital signal processor, a programmable logic devicesuch as a FPGA, PLD, PLA or PAL, or any other device or arrangement ofdevices that is capable of implementing the steps of the processes ofthe invention.

The processing machine used to implement the invention may utilize asuitable operating system. Thus, embodiments of the invention mayinclude a processing machine running the iOS operating system, the OS Xoperating system, the Android operating system, the Microsoft Windows™operating system, the Unix operating system, the Linux operating system,the Xenix operating system, the IBM AIX™ operating system, theHewlett-Packard UX™ operating system, the Novell Netware™ operatingsystem, the Sun Microsystems Solaris™ operating system, the OS/2™operating system, the BeOS™ operating system, the Macintosh operatingsystem, the Apache operating system, an OpenStep™ operating system oranother operating system or platform.

It is appreciated that in order to practice the method of the inventionas described above, it is not necessary that the processors and/or thememories of the processing machine be physically located in the samegeographical place. That is, each of the processors and the memoriesused by the processing machine may be located in geographically distinctlocations and connected so as to communicate in any suitable manner.Additionally, it is appreciated that each of the processor and/or thememory may be composed of different physical pieces of equipment.Accordingly, it is not necessary that the processor be one single pieceof equipment in one location and that the memory be another single pieceof equipment in another location. That is, it is contemplated that theprocessor may be two pieces of equipment in two different physicallocations. The two distinct pieces of equipment may be connected in anysuitable manner. Additionally, the memory may include two or moreportions of memory in two or more physical locations.

To explain further, processing, as described above, is performed byvarious components and various memories. However, it is appreciated thatthe processing performed by two distinct components as described abovemay, in accordance with a further embodiment of the invention, beperformed by a single component. Further, the processing performed byone distinct component as described above may be performed by twodistinct components. In a similar manner, the memory storage performedby two distinct memory portions as described above may, in accordancewith a further embodiment of the invention, be performed by a singlememory portion. Further, the memory storage performed by one distinctmemory portion as described above may be performed by two memoryportions.

Further, various technologies may be used to provide communicationbetween the various processors and/or memories, as well as to allow theprocessors and/or the memories of the invention to communicate with anyother entity; i.e., so as to obtain further instructions or to accessand use remote memory stores, for example. Such technologies used toprovide such communication might include a network, the Internet,Intranet, Extranet, LAN, an Ethernet, wireless communication via celltower or satellite, or any client server system that providescommunication, for example. Such communications technologies may use anysuitable protocol such as TCP/IP, UDP, or OSI, for example.

As described above, a set of instructions may be used in the processingof the invention. The set of instructions may be in the form of aprogram or software. The software may be in the form of system softwareor application software, for example. The software might also be in theform of a collection of separate programs, a program module within alarger program, or a portion of a program module, for example. Thesoftware used might also include modular programming in the form ofobject oriented programming The software tells the processing machinewhat to do with the data being processed.

Further, it is appreciated that the instructions or set of instructionsused in the implementation and operation of the invention may be in asuitable form such that the processing machine may read theinstructions. For example, the instructions that form a program may bein the form of a suitable programming language, which is converted tomachine language or object code to allow the processor or processors toread the instructions. That is, written lines of programming code orsource code, in a particular programming language, are converted tomachine language using a compiler, assembler or interpreter. The machinelanguage is binary coded machine instructions that are specific to aparticular type of processing machine, i.e., to a particular type ofcomputer, for example. The computer understands the machine language.

Any suitable programming language may be used in accordance with thevarious embodiments of the invention. Illustratively, the programminglanguage used may include assembly language, Ada, APL, Basic, C, C++,COBOL, dBase, Forth, Fortran, Java, Modula-2, Pascal, Prolog, REXX,Visual Basic, and/or JavaScript, for example. Further, it is notnecessary that a single type of instruction or single programminglanguage be utilized in conjunction with the operation of the system andmethod of the invention. Rather, any number of different programminglanguages may be utilized as is necessary and/or desirable.

Also, the instructions and/or data used in the practice of the inventionmay utilize any compression or encryption technique or algorithm, as maybe desired. An encryption module might be used to encrypt data. Further,files or other data may be decrypted using a suitable decryption module,for example.

As described above, the invention may illustratively be embodied in theform of a processing machine, including a computer or computer system,for example, that includes at least one memory. It is to be appreciatedthat the set of instructions, i.e., the software for example, thatenables the computer operating system to perform the operationsdescribed above may be contained on any of a wide variety of media ormedium, as desired. Further, the data that is processed by the set ofinstructions might also be contained on any of a wide variety of mediaor medium. That is, the particular medium, i.e., the memory in theprocessing machine, utilized to hold the set of instructions and/or thedata used in the invention may take on any of a variety of physicalforms or transmissions, for example. Illustratively, the medium may bein the form of paper, paper transparencies, a compact disk, a DVD, anintegrated circuit, a hard disk, a floppy disk, an optical disk, amagnetic tape, a RAM, a ROM, a PROM, an EPROM, a wire, a cable, a fiber,a communications channel, a satellite transmission, a memory card, a SIMcard, or other remote transmission, as well as any other medium orsource of data that may be read by the processors of the invention.

Further, the memory or memories used in the processing machine thatimplements the invention may be in any of a wide variety of forms toallow the memory to hold instructions, data, or other information, as isdesired. Thus, the memory might be in the form of a database to holddata. The database might use any desired arrangement of files such as aflat file arrangement or a relational database arrangement, for example.

In the system and method of the invention, a variety of “userinterfaces” may be utilized to allow a user to interface with theprocessing machine or machines that are used to implement the invention.As used herein, a user interface includes any hardware, software, orcombination of hardware and software used by the processing machine thatallows a user to interact with the processing machine. A user interfacemay be in the form of a dialogue screen for example. A user interfacemay also include any of a mouse, touch screen, keyboard, keypad, voicereader, voice recognizer, dialogue screen, menu box, list, checkbox,toggle switch, a pushbutton or any other device that allows a user toreceive information regarding the operation of the processing machine asit processes a set of instructions and/or provides the processingmachine with information. Accordingly, the user interface is any devicethat provides communication between a user and a processing machine. Theinformation provided by the user to the processing machine through theuser interface may be in the form of a command, a selection of data, orsome other input, for example.

As discussed above, a user interface is utilized by the processingmachine that performs a set of instructions such that the processingmachine processes data for a user. The user interface is typically usedby the processing machine for interacting with a user either to conveyinformation or receive information from the user. However, it should beappreciated that in accordance with some embodiments of the system andmethod of the invention, it is not necessary that a human user actuallyinteract with a user interface used by the processing machine of theinvention. Rather, it is also contemplated that the user interface ofthe invention might interact, i.e., convey and receive information, withanother processing machine, rather than a human user. Accordingly, theother processing machine might be characterized as a user. Further, itis contemplated that a user interface utilized in the system and methodof the invention may interact partially with another processing machineor processing machines, while also interacting partially with a humanuser.

It will be readily understood by those persons skilled in the art thatthe present invention is susceptible to broad utility and application.Many embodiments and adaptations of the present invention other thanthose herein described, as well as many variations, modifications andequivalent arrangements, will be apparent from or reasonably suggestedby the present invention and foregoing description thereof, withoutdeparting from the substance or scope of the invention.

Accordingly, while the present invention has been described here indetail in relation to its exemplary embodiments, it is to be understoodthat this disclosure is only illustrative and exemplary of the presentinvention and is made to provide an enabling disclosure of theinvention. Accordingly, the foregoing disclosure is not intended to beconstrued or to limit the present invention or otherwise to exclude anyother such embodiments, adaptations, variations, modifications orequivalent arrangements.

What is claimed is:
 1. A method for hierarchical dual-dynamic exceptionmanagement, comprising: an exception management system comprising atleast one computer processor configuring a hierarchical exceptionprofile comprising a plurality of exceptions, wherein at least one ofthe exceptions comprises a plurality of sub-exceptions, each exceptionand sub-exception having at least an exception metric, a high-levelstatus calculation, and an exception explain calculation; the exceptionmanagement system receiving, from a plurality of external data sources,net-asset value data, the net-asset value data comprising a plurality ofcomponents and sub-components, each component associated with anexception, and each sub-component associated with a sub-exception; theexception management system calculating a current daily value ofinterest for the net-asset value data; the exception management systemidentifying at least one exception from the hierarchical exceptionprofile based on the current daily value of interest; the exceptionmanagement system calculating a high-level status and an explain for theidentified exception from the hierarchical exception profile; theexception management system publishing the identified exception, thecalculated high level status, and the calculated explain to at least oneof an internal system and an external system; and the exceptionmanagement system checking an entitlement of a user accessing theexception, the calculated high level status, and the calculated explainbefore displaying results to the user.
 2. The method of claim 1, whereinthe plurality of external data sources comprise intra-day data sourcesand end-of-day data sources.
 3. The method of claim 1, wherein theplurality of net-asset value data comprises at least one of fund data,portfolio data, income data, expense data, foreign exchange data, andbenchmark data.
 4. The method of claim 1, wherein the receipt ofnet-asset value data triggers an alert that new net-access value data isreceived.
 5. The method of claim 1, wherein the high-level statuscomprises a at least a red level, an amber level, and a green level. 6.The method of claim 1, wherein the step of calculating a high-levelstatus for an exception based on the daily value of interest comprises:setting a baseline based on historical observations of the daily valueof interest; and setting the high-level status for the exception to anelevated status when the current daily value of interest exceeds thebaseline.
 7. The method of claim 6, wherein there is first threshold fornegative values, and a second threshold for positive values.
 8. Themethod of claim 1, further comprising: wherein the explain comprises aroot cause for the exception.
 9. The method of claim 8, furthercomprising: the exception management system providing an internaloperational analyst an exception, a high-level status for the exception,and the explain; the exception management system receiving feedback fromthe internal operational analyst for the exception; the exceptionmanagement system providing a machine learning component with thefeedback; and the machine learning component of the exception managementsystem automatically adjusting the threshold for the high-level status.10. The method of claim 9, wherein the threshold is increased inresponse to a number of false positives above a predetermined number.11. The method of claim 9, further comprising: the exception managementsystem automatically adjusting a frequency of threshold adjustment. 12.The method of claim 1, further comprising: the exception managementsystem receiving an override of the exception.
 13. The method of claim1, further comprising: the exception management system receiving asignoff from the internal operational analyst for a funds alert.
 14. Themethod of claim 1, further comprising: the exception management systemautomatically persisting the metric value, RAG value and RAG status, andthe explain.
 15. The method of claim 1, wherein the hierarchicalexception profile is configurable based on income.
 16. The method ofclaim 1, wherein the hierarchical exception profile is configurablebased on bond income.
 17. The method of claim 1, wherein thehierarchical exception profile is configurable based on equity income.18. The method of claim 1, wherein the hierarchical exception profile isconfigurable based on margin calls and derivatives.